{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e50b9f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.ar_model import AutoReg\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.tsa.arima_process import ArmaProcess\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from pmdarima.arima import auto_arima\n",
    "from pmdarima.arima import ADFTest\n",
    "%matplotlib inline\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995bc0e1",
   "metadata": {},
   "source": [
    "### 4. 基于统计学的时间序列分析方法\n",
    "#### 4.1 自回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98e27817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入销售数据，绘制原始图像\n",
    "sales_data = pd.read_csv('data/retail_sales.csv')\n",
    "sales_data['date']=pd.to_datetime(sales_data['date'])\n",
    "sales_data.set_index('date', inplace=True)\n",
    "sales_data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b43ee6",
   "metadata": {},
   "source": [
    "我们提到使用AR模型的两个前提假设，相关性和平稳性。\n",
    "因此先来检验相关性。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eaa2abf",
   "metadata": {},
   "source": [
    "检验相关性有两种方法\n",
    "- pandas提供了autocorrelation_plot方法用于检验整体自相关性\n",
    "- 使用pandas的lag_plot方法检查单个时间间隔的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00cd1c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Lag', ylabel='Autocorrelation'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.autocorrelation_plot(sales_data['sales']) \n",
    "# 从自相关性图中可以看到在lag<=12时，acf值在临界点以外，表现出极强的相关性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c41cba91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='y(t)', ylabel='y(t + 12)'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将上图中相关性最强的lag=12单独画散点图，观察相关性\n",
    "pd.plotting.lag_plot(sales_data['sales'],lag=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5109a7",
   "metadata": {},
   "source": [
    "第二步检查平稳性，一个最快捷的方法之一是使用statsmodels中的seasonal_decompose方法进行趋势项和季节项的分解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "182440b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据集存在明显趋势项（图二单调递增）和季节项（图三周期性变化）\n",
    "decomposed = seasonal_decompose(sales_data['sales'], model='additive')\n",
    "x = decomposed.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859730dd",
   "metadata": {},
   "source": [
    "幸好statsmodel包的AutoReg方法增加了对趋势和季节项特征的处理，直接使用该方法即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "337a593e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\skywater\\pycharmprojects\\personal\\py39\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "c:\\users\\skywater\\pycharmprojects\\personal\\py39\\lib\\site-packages\\statsmodels\\tsa\\ar_model.py:248: FutureWarning: The parameter names will change after 0.12 is released. Set old_names to False to use the new names now. Set old_names to True to use the old names. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 划分训练集合测试集\n",
    "X = sales_data['sales']\n",
    "train_data = X[1:len(X)-12]\n",
    "test_data = X[len(X)-12:]  #以最后十二个点作为待预测值\n",
    "\n",
    "# 训练AR模型\n",
    "model = AutoReg(train_data,lags=15,missing='drop',trend='t',seasonal=True) \n",
    "model_fitted = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "7c3a3c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>AutoReg Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>sales</td>       <th>  No. Observations:  </th>    <td>59</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>Seas. AutoReg(15)</td> <th>  Log Likelihood     </th> <td>-422.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td>  <th>  S.D. of innovations</th> <td>3621.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 22 Jun 2021</td>  <th>  AIC                </th>  <td>17.707</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>22:47:53</td>      <th>  BIC                </th>  <td>18.883</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>02-01-2011</td>     <th>  HQIC               </th>  <td>18.144</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 09-01-2014</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend</th>       <td>  839.9681</td> <td>  362.378</td> <td>    2.318</td> <td> 0.020</td> <td>  129.721</td> <td> 1550.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.0</th>  <td> 2.213e+05</td> <td> 8.34e+04</td> <td>    2.653</td> <td> 0.008</td> <td> 5.78e+04</td> <td> 3.85e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.1</th>  <td> 2.808e+05</td> <td> 8.08e+04</td> <td>    3.476</td> <td> 0.001</td> <td> 1.22e+05</td> <td> 4.39e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.2</th>  <td> 1.924e+05</td> <td>  8.7e+04</td> <td>    2.212</td> <td> 0.027</td> <td> 2.19e+04</td> <td> 3.63e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.3</th>  <td> 1.689e+05</td> <td> 8.69e+04</td> <td>    1.945</td> <td> 0.052</td> <td>-1331.228</td> <td> 3.39e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.4</th>  <td> 2.263e+05</td> <td> 8.29e+04</td> <td>    2.728</td> <td> 0.006</td> <td> 6.37e+04</td> <td> 3.89e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.5</th>  <td> 2.335e+05</td> <td> 7.98e+04</td> <td>    2.926</td> <td> 0.003</td> <td> 7.71e+04</td> <td>  3.9e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.6</th>  <td>  2.09e+05</td> <td> 8.26e+04</td> <td>    2.530</td> <td> 0.011</td> <td> 4.71e+04</td> <td> 3.71e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.7</th>  <td> 2.318e+05</td> <td> 8.32e+04</td> <td>    2.785</td> <td> 0.005</td> <td> 6.87e+04</td> <td> 3.95e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.8</th>  <td>  2.33e+05</td> <td> 8.41e+04</td> <td>    2.771</td> <td> 0.006</td> <td> 6.82e+04</td> <td> 3.98e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.9</th>  <td> 2.224e+05</td> <td>  8.4e+04</td> <td>    2.648</td> <td> 0.008</td> <td> 5.78e+04</td> <td> 3.87e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.10</th> <td> 1.901e+05</td> <td> 8.46e+04</td> <td>    2.246</td> <td> 0.025</td> <td> 2.42e+04</td> <td> 3.56e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seasonal.11</th> <td> 2.123e+05</td> <td> 8.59e+04</td> <td>    2.473</td> <td> 0.013</td> <td>  4.4e+04</td> <td> 3.81e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L1</th>    <td>    0.2604</td> <td>    0.155</td> <td>    1.685</td> <td> 0.092</td> <td>   -0.042</td> <td>    0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L2</th>    <td>    0.1237</td> <td>    0.158</td> <td>    0.785</td> <td> 0.433</td> <td>   -0.185</td> <td>    0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L3</th>    <td>    0.0379</td> <td>    0.150</td> <td>    0.252</td> <td> 0.801</td> <td>   -0.256</td> <td>    0.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L4</th>    <td>   -0.2515</td> <td>    0.149</td> <td>   -1.691</td> <td> 0.091</td> <td>   -0.543</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L5</th>    <td>    0.2431</td> <td>    0.163</td> <td>    1.496</td> <td> 0.135</td> <td>   -0.075</td> <td>    0.562</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L6</th>    <td>   -0.1179</td> <td>    0.163</td> <td>   -0.722</td> <td> 0.470</td> <td>   -0.438</td> <td>    0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L7</th>    <td>   -0.1311</td> <td>    0.164</td> <td>   -0.799</td> <td> 0.424</td> <td>   -0.453</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L8</th>    <td>   -0.0212</td> <td>    0.196</td> <td>   -0.108</td> <td> 0.914</td> <td>   -0.406</td> <td>    0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L9</th>    <td>    0.2763</td> <td>    0.201</td> <td>    1.374</td> <td> 0.169</td> <td>   -0.118</td> <td>    0.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L10</th>   <td>    0.0443</td> <td>    0.200</td> <td>    0.222</td> <td> 0.825</td> <td>   -0.347</td> <td>    0.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L11</th>   <td>    0.1980</td> <td>    0.203</td> <td>    0.975</td> <td> 0.330</td> <td>   -0.200</td> <td>    0.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L12</th>   <td>    0.1034</td> <td>    0.202</td> <td>    0.512</td> <td> 0.609</td> <td>   -0.292</td> <td>    0.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L13</th>   <td>   -0.4173</td> <td>    0.206</td> <td>   -2.029</td> <td> 0.042</td> <td>   -0.820</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L14</th>   <td>    0.0320</td> <td>    0.192</td> <td>    0.166</td> <td> 0.868</td> <td>   -0.345</td> <td>    0.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sales.L15</th>   <td>    0.0085</td> <td>    0.206</td> <td>    0.041</td> <td> 0.967</td> <td>   -0.396</td> <td>    0.413</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>    <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th>  <td>          -0.9178</td> <td>          -0.4910j</td> <td>           1.0409</td> <td>          -0.4218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th>  <td>          -0.9178</td> <td>          +0.4910j</td> <td>           1.0409</td> <td>           0.4218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th>  <td>          -1.1752</td> <td>          -0.0000j</td> <td>           1.1752</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th>  <td>          -0.6070</td> <td>          -0.8247j</td> <td>           1.0241</td> <td>          -0.3510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th>  <td>          -0.6070</td> <td>          +0.8247j</td> <td>           1.0241</td> <td>           0.3510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.6</th>  <td>          -0.0849</td> <td>          -1.1181j</td> <td>           1.1213</td> <td>          -0.2621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.7</th>  <td>          -0.0849</td> <td>          +1.1181j</td> <td>           1.1213</td> <td>           0.2621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.8</th>  <td>           0.3804</td> <td>          -0.9597j</td> <td>           1.0323</td> <td>          -0.1899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.9</th>  <td>           0.3804</td> <td>          +0.9597j</td> <td>           1.0323</td> <td>           0.1899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.10</th> <td>           0.8226</td> <td>          -0.5979j</td> <td>           1.0170</td> <td>          -0.1000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.11</th> <td>           0.8226</td> <td>          +0.5979j</td> <td>           1.0170</td> <td>           0.1000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.12</th> <td>           1.1469</td> <td>          -0.1677j</td> <td>           1.1591</td> <td>          -0.0231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.13</th> <td>           1.1469</td> <td>          +0.1677j</td> <td>           1.1591</td> <td>           0.0231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.14</th> <td>           5.1284</td> <td>          -0.0000j</td> <td>           5.1284</td> <td>          -0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.15</th> <td>          -9.1838</td> <td>          -0.0000j</td> <td>           9.1838</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            AutoReg Model Results                             \n",
       "==============================================================================\n",
       "Dep. Variable:                  sales   No. Observations:                   59\n",
       "Model:              Seas. AutoReg(15)   Log Likelihood                -422.998\n",
       "Method:               Conditional MLE   S.D. of innovations           3621.518\n",
       "Date:                Tue, 22 Jun 2021   AIC                             17.707\n",
       "Time:                        22:47:53   BIC                             18.883\n",
       "Sample:                    02-01-2011   HQIC                            18.144\n",
       "                         - 09-01-2014                                         \n",
       "===============================================================================\n",
       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "trend         839.9681    362.378      2.318      0.020     129.721    1550.216\n",
       "seasonal.0   2.213e+05   8.34e+04      2.653      0.008    5.78e+04    3.85e+05\n",
       "seasonal.1   2.808e+05   8.08e+04      3.476      0.001    1.22e+05    4.39e+05\n",
       "seasonal.2   1.924e+05    8.7e+04      2.212      0.027    2.19e+04    3.63e+05\n",
       "seasonal.3   1.689e+05   8.69e+04      1.945      0.052   -1331.228    3.39e+05\n",
       "seasonal.4   2.263e+05   8.29e+04      2.728      0.006    6.37e+04    3.89e+05\n",
       "seasonal.5   2.335e+05   7.98e+04      2.926      0.003    7.71e+04     3.9e+05\n",
       "seasonal.6    2.09e+05   8.26e+04      2.530      0.011    4.71e+04    3.71e+05\n",
       "seasonal.7   2.318e+05   8.32e+04      2.785      0.005    6.87e+04    3.95e+05\n",
       "seasonal.8    2.33e+05   8.41e+04      2.771      0.006    6.82e+04    3.98e+05\n",
       "seasonal.9   2.224e+05    8.4e+04      2.648      0.008    5.78e+04    3.87e+05\n",
       "seasonal.10  1.901e+05   8.46e+04      2.246      0.025    2.42e+04    3.56e+05\n",
       "seasonal.11  2.123e+05   8.59e+04      2.473      0.013     4.4e+04    3.81e+05\n",
       "sales.L1        0.2604      0.155      1.685      0.092      -0.042       0.563\n",
       "sales.L2        0.1237      0.158      0.785      0.433      -0.185       0.433\n",
       "sales.L3        0.0379      0.150      0.252      0.801      -0.256       0.332\n",
       "sales.L4       -0.2515      0.149     -1.691      0.091      -0.543       0.040\n",
       "sales.L5        0.2431      0.163      1.496      0.135      -0.075       0.562\n",
       "sales.L6       -0.1179      0.163     -0.722      0.470      -0.438       0.202\n",
       "sales.L7       -0.1311      0.164     -0.799      0.424      -0.453       0.190\n",
       "sales.L8       -0.0212      0.196     -0.108      0.914      -0.406       0.363\n",
       "sales.L9        0.2763      0.201      1.374      0.169      -0.118       0.670\n",
       "sales.L10       0.0443      0.200      0.222      0.825      -0.347       0.436\n",
       "sales.L11       0.1980      0.203      0.975      0.330      -0.200       0.596\n",
       "sales.L12       0.1034      0.202      0.512      0.609      -0.292       0.499\n",
       "sales.L13      -0.4173      0.206     -2.029      0.042      -0.820      -0.014\n",
       "sales.L14       0.0320      0.192      0.166      0.868      -0.345       0.409\n",
       "sales.L15       0.0085      0.206      0.041      0.967      -0.396       0.413\n",
       "                                    Roots                                     \n",
       "==============================================================================\n",
       "                   Real          Imaginary           Modulus         Frequency\n",
       "------------------------------------------------------------------------------\n",
       "AR.1            -0.9178           -0.4910j            1.0409           -0.4218\n",
       "AR.2            -0.9178           +0.4910j            1.0409            0.4218\n",
       "AR.3            -1.1752           -0.0000j            1.1752           -0.5000\n",
       "AR.4            -0.6070           -0.8247j            1.0241           -0.3510\n",
       "AR.5            -0.6070           +0.8247j            1.0241            0.3510\n",
       "AR.6            -0.0849           -1.1181j            1.1213           -0.2621\n",
       "AR.7            -0.0849           +1.1181j            1.1213            0.2621\n",
       "AR.8             0.3804           -0.9597j            1.0323           -0.1899\n",
       "AR.9             0.3804           +0.9597j            1.0323            0.1899\n",
       "AR.10            0.8226           -0.5979j            1.0170           -0.1000\n",
       "AR.11            0.8226           +0.5979j            1.0170            0.1000\n",
       "AR.12            1.1469           -0.1677j            1.1591           -0.0231\n",
       "AR.13            1.1469           +0.1677j            1.1591            0.0231\n",
       "AR.14            5.1284           -0.0000j            5.1284           -0.0000\n",
       "AR.15           -9.1838           -0.0000j            9.1838           -0.5000\n",
       "------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看模型结果\n",
    "model_fitted.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "092fba83",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\skywater\\pycharmprojects\\personal\\py39\\lib\\site-packages\\statsmodels\\tsa\\deterministic.py:147: UserWarning: Only PeriodIndexes, DatetimeIndexes with a frequency set, RangesIndexes, and Int64Indexes with a unit increment support extending. The index is set will contain the position relative to the data length.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 预测测试集\n",
    "predictions = model_fitted.predict(\n",
    "    start=len(train_data), \n",
    "    end=len(train_data) + len(test_data)-1, \n",
    "    dynamic=False)  # dynamic参数表示是否用预测值动态预测下一个时刻的值\n",
    "\n",
    "# 比较真实值和预测值\n",
    "compare_df = pd.concat(\n",
    "    [sales_data['sales'].tail(12),\n",
    "    predictions], axis=1).rename(\n",
    "    columns={'sales': 'actual', 0:'predicted'})\n",
    "compare_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdf4510",
   "metadata": {},
   "source": [
    "#### 4.2 移动平均模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095f8d4d",
   "metadata": {},
   "source": [
    "##### 模拟MA序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9ced0ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1de7bd31e50>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ar,ma必须以array的形式输入，且第一位表示lag=0，通常这个值会设为1\n",
    "ar = np.array([1])  # ar项只有一个间隔=0的值表示是一个纯MA序列\n",
    "ma = np.array([1, -0.9]) # ma序列有两个值，第一个是常数项，第二个是前一个时刻的系数，这是一个MA(1)模型\n",
    "MA_object = ArmaProcess(ar, ma)\n",
    "simulated_data = MA_object.generate_sample(nsample=1000)\n",
    "plt.plot(simulated_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2dbc8054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出acf图像后看到，如上文所说，对于一个MA(1)序列，从时间间隔大于等于2开始，相关系数断崖式下降\n",
    "plot_acf(simulated_data, lags=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "606f5000",
   "metadata": {},
   "source": [
    "##### 模型拟合与评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e097a17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                 1000\n",
      "Model:                     ARMA(0, 1)   Log Likelihood               -1442.463\n",
      "Method:                       css-mle   S.D. of innovations              1.023\n",
      "Date:                Wed, 23 Jun 2021   AIC                           2890.927\n",
      "Time:                        20:46:29   BIC                           2905.650\n",
      "Sample:                             0   HQIC                          2896.523\n",
      "                                                                              \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0006      0.003     -0.175      0.861      -0.007       0.006\n",
      "ma.L1.y       -0.8937      0.016    -57.081      0.000      -0.924      -0.863\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "MA.1            1.1190           +0.0000j            1.1190            0.0000\n",
      "-----------------------------------------------------------------------------\n",
      "[-6.05711676e-04 -8.93691112e-01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\skywater\\pycharmprojects\\personal\\py39\\lib\\site-packages\\statsmodels\\tsa\\arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# order=(0,1)表示这是一个纯MA(1)模型\n",
    "mod = ARMA(simulated_data_1, order=(0, 1))  \n",
    "res = mod.fit()\n",
    "\n",
    "# 观察模型拟合结果， 系数为-0.8937，和我们创建时间序列的系数-0.9很接近\n",
    "print(res.summary())\n",
    "\n",
    "# 打印拟合值\n",
    "print(res.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d606942b",
   "metadata": {},
   "source": [
    "##### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "30a278a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.plot_predict(start=990, end=1010)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f86fbcf2",
   "metadata": {},
   "source": [
    "可以看到MA模型仅仅对样本内的值有实际预测效果，对样本外的值会用统一用整体均值来预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ce4448",
   "metadata": {},
   "source": [
    "#### 4.3 ARIMA模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc3dcd3",
   "metadata": {},
   "source": [
    "**手动拟合法**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "64d0bf88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21b06604af0>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 模拟ARMA数据，在完成最终拟合前，请假装不知道样本时间序列的参数\n",
    "import statsmodels.api as sm\n",
    "ar = np.array([1,-0.8, 0.4])  \n",
    "ma = np.array([1,-0.7]) \n",
    "arma_process = sm.tsa.ArmaProcess(ar, ma)\n",
    "y = arma_process.generate_sample(1000)\n",
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2bf9af37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# step1 - 画出acf，pacf曲线，根据经验规则，没有明显的cutoff，模型应当包含AR和MA项\n",
    "plot_acf(y)\n",
    "plot_pacf(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ca6d4819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step2，用最简单的arima(1,0,1)尝试拟合\n",
    "mod = ARIMA(y, order=(1,0,1))  \n",
    "res = mod.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "01bcb2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# step3，画出拟合残差的acf和pacf\n",
    "plot_acf(res.resid)\n",
    "plot_pacf(res.resid)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "114c7c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PI4W+4Mlte7nnyZd5ctteCn0xqnrWOLbsPsLx3sKAsuO9BbbuPlKnFlk9JDPlcvjYKXoLfSN+fpB2aBX6gj94+AV+uOsIJ04VmDShlXfNmso9K64c8JH8dFZvy+63610xayp/sfzteuXWVSSw+hq9CzHMSr546kQmTWjh+Km3j4FJE1ronDKR7jeOndk+I9nPU9i2kH/7Dreea6FjYhszp04ak9dOJtB3Hz7OoWOn6t2MhrVx50E29xzm5OniAX/8VIHNuw7z+MZdXDX/wgH1Xto1sN5Luw7znRcG1rPGMmfaeVza2cHWPUeIgIltLVza2cHcC8+j5+DxejfPShQ6YswC3VMu48SrB96i9/TATzC9p/t49cBbI6pn505fX7Bx50Ee39jDxp0H6SszBdbSIu5cdjmzp02ms6OdP/jAIu5cdjktPiE6riQzQrfhLbjofNrbWs6MvAHa21pYcNH5I6pn50ZfX/DF725jx76j9J7uo72thctmdpQN65YWMWVSG1MmtfnTVIm+vmBT9yFePfAWCy46nyVzpyX7RudAHyeWzJ3GZTMHfiS/bGYHS+ZOG1G9lDTyAb+p+xA79h098wZ78nQfO/YdZVP3oTEP7UZeL3lV84aYAgf6ONH/kfzTj2/m5KkCt75vYdkDNG+9VDT6AT/cFNhYBvpYrJd6vEHU8w2xHsZ1oNdrBFKv5eb9SJ63XgojuEYfAddrCqzW66Veb5z1ekOsl3Eb6PXawapZbiMHZqOPbPNq9BFwvabAar1exuKNs5HfEPO2r9bGbaDXa2SWd7mNHvypfJRt9BFwvabAqlkvefa/Wr9BNPob4nDtG0u5LluUdL2k7ZJ2SLqjzOMTJT2aPf68pAU1b2mN1evyvLzLLT3gg4EHfKn+Heeep17msQ093PPUy3zxu9vKXtpWj340uv4DXlkGDHfA57l8MK9q1l//FNiMKRO5av6F5+QTUN71knf/63+DKDXcG0Sl9Zz3+KjX5Zx521drFUfoklqBe4HrgB5gnaQ1EbG1pNptwMGIuEzScuBPgQ+PRYNrpV4js7zLzTuiqddIudYjuHrJOwKu9RRTo18emne95N3/8o6U867nakb89bics14DHlX6mquka4G7IuKXs/ufAYiIPymp80RW5weS2oDXgM4Y5sWnz788rrvzgaobvHVP8bcpFl8ydUD5sd4Cp/vyf/U/IvjJG8c5lv3+hQSTJ7Qyb/pkpJGFTURw9GThzFfmOya2nvVaeZf75onT7Dp0fMDXriWYPW0yUya9/T68/82TvH6096y2dHa0M2PKxLPKdx44BsD8i84bti+V6uXtR3+946cKRAy/nvOsv2rqVaNSf/Nuj7yq3f9qtd2qVen1qtn/IoIfv36Mvggunjqp7HbLu56r3R61Xi+VDNe+6ee3M3lC64hfe/Xvvm9DRHSVeyzPnjgb6C653wNcM1SdiDgt6TBwEfB6aSVJK4GVAB2XvCNX4wcbHOTl5NlxJDFv+mSOnixw8lSBiRWCoVYBl3e5HRNbmTyh9awg7Jg4cEeYNKEVibN2nIlD7DB5d+hK9fL24+jJwpk+QLGdx08VOHqyMODAq/YNotZBWOnxEyV9eLstcPJUYUQBUu3+V6vtVmx35eMj7+tVs/9J4tLO4T+B5F3PeY+PvP3ol2d/ybP+qm1frZzTk6IRcR9wH0BXV1c8+jvX1uy1t+05wqFjp858ZOst9BFRHEFcMHn0189++vHNnDhV4EM/M6vsR8+NOw9yz1Mvn7kfUfxBrF959+wxPYvf6FebPL6xh8c29AwsDLj20ov4tavmnCnKu/6qXc93//0WAFZ96IpR9aN/uaVTJBPbWrj1fQvPWm6e/aVean181Hr/q3Y913oqr9L+Us36G6p9MzraWXTxlBG3cfXvDv1YnkDfBcwtuT8nKytXpyebcrkAOFBVK2ukf06v/12+VtfP9n98uuepl0c9p5dXS4u4av6FFa8Zv3PZ5Q07R13rcwb1usywfw54cHANNQdcaX+pl1ofH7Xe//Ku5/5lVzo+qtHXF7x54jQnThXYuPPgsOcM8qy/WrcvjzyBvg5YJGkhxeBeDvzGoDprgFuAHwC/Djw13Pz5WBqr62crbcB6nuSqx46TV94DdCx+aybPAZpX3uCqdWDWWr0GHtW8Vj0GKPUcuNVSxUDP5sRvB54AWoEHImKLpLuB9RGxBrgfeEjSDuANiqFfF7UO1rwbsJqRxXiS9wDNu/7qOVLOE1yNfsA3+tU1UJ8BSjMM3PLINYceEWuBtYPKVpXcPgH8u9o2bWRqHax5N2CjT33UUy2njhp9pNzoB7wHHuWlMnBL7puiKc3pjTd5118jj5Qb/YD3wKO8VAZuyQU6pDGnZ6NTr5FyM+wvHnicLZWBW5KBXmuNvAGtvHqOlL2/NJ9meCPOw4FuSUrlALVzJ4U3Yge6JSuFA9SsGskEesfENlpG+XsetRTU5TL8qpX7tkDZskH9qc+3DM69Ru7ncPvYmZ9aGFD29r3RdquR10t1Rt6Rka6D1jH8lJhMoM+dfm5+dMfMrFHl+j10MzNrfA50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEarTX4pD0n5g5wifPgN4vYbNqRf3o7G4H43F/ShvfkR0lnugboE+GpLWR0RXvdsxWu5HY3E/Gov7UT1PuZiZJcKBbmaWiGYN9Pvq3YAacT8ai/vRWNyPKjXlHLqZmZ2tWUfoZmY2iAPdzCwRTRfokq6XtF3SDkl31Ls9IyXpVUkvSdokaX2925OXpAck7ZP0w5Ky6ZK+L+nl7P+G/yOeQ/TjLkm7sm2ySdIN9WxjJZLmSnpa0lZJWyR9Iitvqu0xTD+abXtMkvT/JL2Y9eMLWflCSc9nmfWopPYxa0MzzaFLagV+BFwH9ADrgBURsbWuDRsBSa8CXRHRVF+ckPTzwFHg6xHxrqzsz4A3IuK/Zm+yF0bEp+vZzkqG6MddwNGI+G/1bFteki4BLomIjZKmABuAXwVupYm2xzD9uJnm2h4Czo+Io5ImAP8MfAL4FPB4RDwi6a+AFyPiq2PRhmYboV8N7IiIVyKiF3gEuKnObRpXIuJZ4I1BxTcBD2a3H6R4MDa0IfrRVCJiT0RszG6/CWwDZtNk22OYfjSVKDqa3Z2Q/QvgA8BjWfmYbo9mC/TZQHfJ/R6acMNnAviepA2SVta7MaN0cUTsyW6/Blxcz8aM0u2SNmdTMg09VVFK0gLgSuB5mnh7DOoHNNn2kNQqaROwD/g+8C/AoYg4nVUZ08xqtkBPyfsj4ipgGfB72RRA04viHF7zzOMN9FXgHcASYA/wpbq2JidJHcC3gU9GxJHSx5ppe5TpR9Ntj4goRMQSYA7FGYV/dS6X32yBvguYW3J/TlbWdCJiV/b/PuB/Udz4zWpvNg/aPx+6r87tGZGI2JsdkH3A39AE2ySbq/028HcR8XhW3HTbo1w/mnF79IuIQ8DTwLXANElt2UNjmlnNFujrgEXZWeN2YDmwps5tqpqk87OTP0g6H/gl4IfDP6uhrQFuyW7fAvzvOrZlxPpDMPNvaPBtkp2Eux/YFhFfLnmoqbbHUP1owu3RKWladnsyxYs3tlEM9l/Pqo3p9miqq1wAskuXvgK0Ag9ExB/Xt0XVk3QpxVE5QBvwzWbph6SHgaUUfxJ0L/B54DvAamAexZ9EvjkiGvqE4xD9WErx430ArwK/UzIX3XAkvR/4J+AloC8rvpPi/HPTbI9h+rGC5toe76Z40rOV4mB5dUTcnR3vjwDTgReAj0TEyTFpQ7MFupmZlddsUy5mZjYEB7qZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmifj/+J/dnJPk0eIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# step4，从上面对residual绘制acf和pacf可以看到，还存在有显著的自相关性，说明最开始尝试的ARIMA(1,0,1)模型并没有充分表达原数据  \n",
    "# 尝试增加模型复杂度\n",
    "a2m1 = ARIMA(y, order=(2,0,1)).fit()  \n",
    "plot_acf(a2m1.resid)\n",
    "plot_pacf(a2m1.resid)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ee371498",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step5，通过将AR项从一阶改成二阶，这个更复杂的模型取得了不错的效果，残差的acf和pacf几乎看不到其他相关性存在  \n",
    "# 为了验证是否还能获得更好的模型，继续修改参数进行测试，一个简单快速的验证方式是计算真实值和预测值的相关系数\n",
    "def model_performance(p,d,q):\n",
    "    model = ARIMA(y, order=(p,d,q)).fit()  \n",
    "    df = pd.DataFrame({'predict':model.predict(),'true':y})\n",
    "    corr = df.corr().loc['predict','true']\n",
    "    print(\"ARIMA({0},{1},{2}) performance: {3} \".format(p,d,q,corr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "155bb1a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1,0,1) performance: 0.23256695743385164 \n",
      "ARIMA(2,0,1) performance: 0.42499463153273376 \n",
      "ARIMA(1,0,2) performance: 0.39161778792506313 \n",
      "ARIMA(2,0,2) performance: 0.42705219696176494 \n",
      "ARIMA(2,1,2) performance: 0.4152659567936595 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\skywater\\pycharmprojects\\personal\\py39\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "model_performance(1,0,1)\n",
    "model_performance(2,0,1)\n",
    "model_performance(1,0,2)\n",
    "model_performance(2,0,2)\n",
    "model_performance(2,1,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7dc0a5",
   "metadata": {},
   "source": [
    "可以看到ARIMA(2,0,1)的效果有了明显提升，而当再增加模型复杂度时，效果没有明显提升。当然这只是一个极为粗略的手动拟合的例子，真实场景则需要更为精细的优化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "69c0538f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00219359,  0.78972449, -0.40471765, -0.68275666,  0.97684689])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARIMA(y, order=(2,0,1)).fit().params # 模型系数 ar1=0.79,ar2=-0.4,ma1=-0.69和最开始我们创建的时间序列系数很接近，获得了不错的拟合效果"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5044f90b",
   "metadata": {},
   "source": [
    "**自动拟合法**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cc4280c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# step1，准备数据\n",
    "sales_data = pd.read_csv('data/retail_sales.csv')\n",
    "sales_data['date']=pd.to_datetime(sales_data['date'])\n",
    "sales_data.set_index('date', inplace=True)\n",
    "sales_data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7899577c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.01, False)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step2，检查平稳性\n",
    "adf_test = ADFTest()\n",
    "adf_test.should_diff(sales_data) #结果表明不平稳，提示我们需要引入差分项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1cdcb563",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step3，划分训练集和测试集\n",
    "train = sales_data[:60]\n",
    "test = sales_data[60:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bf7d047e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(0,1,0)(0,1,0)[12]             : AIC=981.377, Time=0.02 sec\n",
      " ARIMA(1,1,0)(1,1,0)[12]             : AIC=982.734, Time=0.11 sec\n",
      " ARIMA(0,1,1)(0,1,1)[12]             : AIC=982.307, Time=0.14 sec\n",
      " ARIMA(0,1,0)(1,1,0)[12]             : AIC=983.595, Time=0.13 sec\n",
      " ARIMA(0,1,0)(0,1,1)[12]             : AIC=1005.088, Time=0.06 sec\n",
      " ARIMA(0,1,0)(1,1,1)[12]             : AIC=1007.898, Time=0.31 sec\n",
      " ARIMA(1,1,0)(0,1,0)[12]             : AIC=981.328, Time=0.02 sec\n",
      " ARIMA(1,1,0)(0,1,1)[12]             : AIC=982.703, Time=0.13 sec\n",
      " ARIMA(1,1,0)(1,1,1)[12]             : AIC=984.307, Time=0.68 sec\n",
      " ARIMA(2,1,0)(0,1,0)[12]             : AIC=987.645, Time=0.04 sec\n",
      " ARIMA(1,1,1)(0,1,0)[12]             : AIC=982.083, Time=0.19 sec\n",
      " ARIMA(0,1,1)(0,1,0)[12]             : AIC=980.880, Time=0.06 sec\n",
      " ARIMA(0,1,1)(1,1,0)[12]             : AIC=982.336, Time=0.10 sec\n",
      " ARIMA(0,1,1)(1,1,1)[12]             : AIC=983.925, Time=0.46 sec\n",
      " ARIMA(0,1,2)(0,1,0)[12]             : AIC=982.207, Time=0.08 sec\n",
      " ARIMA(1,1,2)(0,1,0)[12]             : AIC=983.139, Time=0.17 sec\n",
      " ARIMA(0,1,1)(0,1,0)[12] intercept   : AIC=982.868, Time=0.08 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,1)(0,1,0)[12]          \n",
      "Total fit time: 2.802 seconds\n"
     ]
    }
   ],
   "source": [
    "# step4，拟合模型\n",
    "arima_model = auto_arima(train, start_p=0, d=1,start_q=0, max_p=5,max_d=5,max_q=5,\n",
    "                         start_P=0, D=1, start_Q=0, max_P=5, max_D=5, max_Q=5, m=12, seasonal=True,trace=True,n_fits=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c4e4856e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                  <td>y</td>                <th>  No. Observations:  </th>    <td>60</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 1)x(0, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-488.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Tue, 29 Jun 2021</td>         <th>  AIC                </th>  <td>980.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>17:09:46</td>             <th>  BIC                </th>  <td>984.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>                <th>  HQIC               </th>  <td>982.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td> - 60</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>               <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.0445</td> <td>    0.035</td> <td>   -1.257</td> <td> 0.209</td> <td>   -0.114</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 5.112e+07</td> <td> 2.95e-11</td> <td> 1.73e+18</td> <td> 0.000</td> <td> 5.11e+07</td> <td> 5.11e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>11.29</td> <th>  Jarque-Bera (JB):  </th> <td>0.40</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th> <td>0.82</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.28</td>  <th>  Skew:              </th> <td>-0.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.62</td>  <th>  Kurtosis:          </th> <td>2.56</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 6.66e+33. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                      \n",
       "===========================================================================================\n",
       "Dep. Variable:                                   y   No. Observations:                   60\n",
       "Model:             SARIMAX(0, 1, 1)x(0, 1, [], 12)   Log Likelihood                -488.440\n",
       "Date:                             Tue, 29 Jun 2021   AIC                            980.880\n",
       "Time:                                     17:09:46   BIC                            984.580\n",
       "Sample:                                          0   HQIC                           982.272\n",
       "                                              - 60                                         \n",
       "Covariance Type:                               opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -0.0445      0.035     -1.257      0.209      -0.114       0.025\n",
       "sigma2      5.112e+07   2.95e-11   1.73e+18      0.000    5.11e+07    5.11e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                  11.29   Jarque-Bera (JB):                 0.40\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.82\n",
       "Heteroskedasticity (H):               1.28   Skew:                            -0.05\n",
       "Prob(H) (two-sided):                  0.62   Kurtosis:                         2.56\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 6.66e+33. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arima_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e9efc0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step5，预测时间序列，并和真实值比较\n",
    "pred = pd.DataFrame(arima_model.predict(n_periods=12),columns=['predicted'],index=test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "2598dc73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train)\n",
    "plt.plot(test,label='true')\n",
    "plt.plot(pred,label='predict')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "37ed5f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5a8614",
   "metadata": {},
   "source": [
    "#### 4.4 向量自回归模型（VAR）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d2ce1748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rgnp</th>\n",
       "      <th>pgnp</th>\n",
       "      <th>ulc</th>\n",
       "      <th>gdfco</th>\n",
       "      <th>gdf</th>\n",
       "      <th>gdfim</th>\n",
       "      <th>gdfcf</th>\n",
       "      <th>gdfce</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>1606.4</td>\n",
       "      <td>1608.3</td>\n",
       "      <td>47.5</td>\n",
       "      <td>36.9</td>\n",
       "      <td>37.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>32.3</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>1637.0</td>\n",
       "      <td>1622.2</td>\n",
       "      <td>47.5</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>1629.5</td>\n",
       "      <td>1636.2</td>\n",
       "      <td>48.7</td>\n",
       "      <td>37.6</td>\n",
       "      <td>37.6</td>\n",
       "      <td>27.1</td>\n",
       "      <td>32.4</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>1643.4</td>\n",
       "      <td>1650.3</td>\n",
       "      <td>48.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>37.8</td>\n",
       "      <td>27.1</td>\n",
       "      <td>32.5</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>1671.6</td>\n",
       "      <td>1664.6</td>\n",
       "      <td>49.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>37.8</td>\n",
       "      <td>27.2</td>\n",
       "      <td>32.4</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rgnp    pgnp   ulc  gdfco   gdf  gdfim  gdfcf  gdfce\n",
       "date                                                              \n",
       "1959-01-01  1606.4  1608.3  47.5   36.9  37.4   26.9   32.3   23.1\n",
       "1959-04-01  1637.0  1622.2  47.5   37.4  37.5   27.0   32.2   23.4\n",
       "1959-07-01  1629.5  1636.2  48.7   37.6  37.6   27.1   32.4   23.4\n",
       "1959-10-01  1643.4  1650.3  48.8   37.7  37.8   27.1   32.5   23.8\n",
       "1960-01-01  1671.6  1664.6  49.1   37.8  37.8   27.2   32.4   23.8"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step1：导入数据，这是一个有八个指标的经济数据\n",
    "df = pd.read_csv('data/Raotbl6.csv', parse_dates=['date'], index_col='date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "57223ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出八个时间序列的图像\n",
    "fig, axes = plt.subplots(nrows=4, ncols=2,figsize=(10,6))\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    data = df[df.columns[i]]\n",
    "    ax.plot(data, color='red', linewidth=1)\n",
    "    ax.set_title(df.columns[i])\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d5be6046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rgnp_x</th>\n",
       "      <th>pgnp_x</th>\n",
       "      <th>ulc_x</th>\n",
       "      <th>gdfco_x</th>\n",
       "      <th>gdf_x</th>\n",
       "      <th>gdfim_x</th>\n",
       "      <th>gdfcf_x</th>\n",
       "      <th>gdfce_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rgnp_y</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0212</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pgnp_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ulc_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdfco_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdf_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdfim_y</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdfcf_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdfce_y</th>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rgnp_x  pgnp_x   ulc_x  gdfco_x   gdf_x  gdfim_x  gdfcf_x  gdfce_x\n",
       "rgnp_y   1.0000  0.0003  0.0001   0.0212  0.0014   0.0620   0.0001   0.0071\n",
       "pgnp_y   0.0000  1.0000  0.0000   0.0000  0.0000   0.0000   0.0000   0.0000\n",
       "ulc_y    0.0000  0.0000  1.0000   0.0002  0.0000   0.0000   0.0000   0.0041\n",
       "gdfco_y  0.0000  0.0000  0.0000   1.0000  0.0000   0.0000   0.0000   0.0000\n",
       "gdf_y    0.0000  0.0000  0.0000   0.0000  1.0000   0.0000   0.0000   0.0000\n",
       "gdfim_y  0.0011  0.0067  0.0014   0.0083  0.0011   1.0000   0.0004   0.0000\n",
       "gdfcf_y  0.0000  0.0000  0.0008   0.0008  0.0000   0.0038   1.0000   0.0009\n",
       "gdfce_y  0.0025  0.0485  0.0000   0.0002  0.0000   0.0000   0.0000   1.0000"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step2：Granger’s Causality Test ， 检验不同序列之间存在互相影响\n",
    "maxlag=12\n",
    "test='ssr_chi2test'\n",
    "variables=df.columns\n",
    "def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False):    \n",
    "    df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n",
    "    for c in df.columns:\n",
    "        for r in df.index:\n",
    "            test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)\n",
    "            p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]\n",
    "            min_p_value = np.min(p_values)\n",
    "            df.loc[r, c] = min_p_value\n",
    "    df.columns = [var + '_x' for var in variables]\n",
    "    df.index = [var + '_y' for var in variables]\n",
    "    return df\n",
    "\n",
    "grangers_causation_matrix(df, variables = df.columns)   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23f8991",
   "metadata": {},
   "source": [
    "在输出结果中，index以y结尾，表示响应变量，column以x结尾，表示预测变量，如果p值小于0.05表明存在格兰杰因果性。  \n",
    "因此根据检验数据，完全有理由使用VAR模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "67591d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step3：ADF测试，检验单个变量是否平稳\n",
    "def adfuller_test(series, signif=0.05, name='', verbose=False):\n",
    "    r = adfuller(series, autolag='AIC')\n",
    "    output = {'test_statistic':round(r[0], 4), 'pvalue':round(r[1], 4), 'n_lags':round(r[2], 4), 'n_obs':r[3]}\n",
    "    p_value = output['pvalue'] \n",
    "    def adjust(val, length= 6): return str(val).ljust(length)\n",
    "\n",
    "    # Print Summary\n",
    "    print(f'    Augmented Dickey-Fuller Test on \"{name}\"', \"\\n   \", '-'*47)\n",
    "    print(f' Null Hypothesis: Data has unit root. Non-Stationary.')\n",
    "    print(f' Significance Level    = {signif}')\n",
    "    print(f' Test Statistic        = {output[\"test_statistic\"]}')\n",
    "    print(f' No. Lags Chosen       = {output[\"n_lags\"]}')\n",
    "\n",
    "    for key,val in r[4].items():\n",
    "        print(f' Critical value {adjust(key)} = {round(val, 3)}')\n",
    "\n",
    "    if p_value <= signif:\n",
    "        print(f\" => P-Value = {p_value}. Rejecting Null Hypothesis.\")\n",
    "        print(f\" => Series is Stationary.\")\n",
    "    else:\n",
    "        print(f\" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis.\")\n",
    "        print(f\" => Series is Non-Stationary.\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4260738b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"rgnp\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 0.6419\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.486\n",
      " Critical value 5%     = -2.886\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.9886. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"pgnp\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 1.2743\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.486\n",
      " Critical value 5%     = -2.886\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.9965. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"ulc\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 1.3967\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.486\n",
      " Critical value 5%     = -2.886\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.9971. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfco\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 0.5762\n",
      " No. Lags Chosen       = 5\n",
      " Critical value 1%     = -3.488\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.987. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdf\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 1.1129\n",
      " No. Lags Chosen       = 7\n",
      " Critical value 1%     = -3.489\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.9953. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfim\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -0.1987\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.486\n",
      " Critical value 5%     = -2.886\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.9387. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfcf\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 1.6693\n",
      " No. Lags Chosen       = 9\n",
      " Critical value 1%     = -3.49\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.581\n",
      " => P-Value = 0.9981. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfce\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -0.8159\n",
      " No. Lags Chosen       = 13\n",
      " Critical value 1%     = -3.492\n",
      " Critical value 5%     = -2.888\n",
      " Critical value 10%    = -2.581\n",
      " => P-Value = 0.8144. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name, column in df.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac53f95",
   "metadata": {},
   "source": [
    "没有一个变量具有平稳性，提示我们需要进一步进行协整检验（cointegration test）。  \n",
    "一般来说进行协整检验的步骤如下：  \n",
    "1.单独检验单个变量是否平稳，使用ADF test, KPSS test, PP test等方法  \n",
    "2.如果发现单个序列不平稳，则需要进一步进行协整检验。进行协整检验的目的是当每个变量本身不平稳，有可能他们在某些线性组合下是平稳的。如果两个时间序列是协整的，则表明他们具有长期的，统计学显著的关联，使用Johansen, Engle-Granger, and Phillips-Ouliaris等方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "bf8d674a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name   ::  Test Stat > C(95%)    =>   Signif  \n",
      " ----------------------------------------\n",
      "rgnp   ::  248.0     > 143.6691  =>   True\n",
      "pgnp   ::  183.12    > 111.7797  =>   True\n",
      "ulc    ::  130.01    > 83.9383   =>   True\n",
      "gdfco  ::  85.28     > 60.0627   =>   True\n",
      "gdf    ::  55.05     > 40.1749   =>   True\n",
      "gdfim  ::  31.59     > 24.2761   =>   True\n",
      "gdfcf  ::  14.06     > 12.3212   =>   True\n",
      "gdfce  ::  0.45      > 4.1296    =>   False\n"
     ]
    }
   ],
   "source": [
    "# step4: 协整检验，检验多变量平稳性\n",
    "def cointegration_test(df, alpha=0.05): \n",
    "    out = coint_johansen(df,-1,5)\n",
    "    d = {'0.90':0, '0.95':1, '0.99':2}\n",
    "    traces = out.lr1\n",
    "    cvts = out.cvt[:, d[str(1-alpha)]]\n",
    "    def adjust(val, length= 6): return str(val).ljust(length)\n",
    "\n",
    "    # Summary\n",
    "    print('Name   ::  Test Stat > C(95%)    =>   Signif  \\n', '--'*20)\n",
    "    for col, trace, cvt in zip(df.columns, traces, cvts):\n",
    "        print(adjust(col), ':: ', adjust(round(trace,2), 9), \">\", adjust(cvt, 8), ' =>  ' , trace > cvt)\n",
    "\n",
    "cointegration_test(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "7bd5e16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step5：划分训练集和测试集\n",
    "nobs = 4  # 最后四个时间点作为测试集\n",
    "df_train, df_test = df[0:-nobs], df[-nobs:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b73b25fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"rgnp\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -5.3448\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.488\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"pgnp\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -1.8282\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.488\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.3666. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"ulc\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -3.4658\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.488\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0089. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfco\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -1.4385\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.489\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.5637. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdf\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -1.1289\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.489\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.7034. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfim\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -4.1256\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.488\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0009. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfcf\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.0545\n",
      " No. Lags Chosen       = 7\n",
      " Critical value 1%     = -3.491\n",
      " Critical value 5%     = -2.888\n",
      " Critical value 10%    = -2.581\n",
      " => P-Value = 0.2632. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfce\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -3.1543\n",
      " No. Lags Chosen       = 7\n",
      " Critical value 1%     = -3.491\n",
      " Critical value 5%     = -2.888\n",
      " Critical value 10%    = -2.581\n",
      " => P-Value = 0.0228. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# step6：使用VAR之间，先差分处理使单个变量变得平稳\n",
    "df_differenced = df_train.diff().dropna()\n",
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "51ff06ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一阶差分后仍然有些变量不平稳，进行二次差分\n",
    "df_differenced = df_differenced.diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "739bed6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"rgnp\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -9.0123\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.489\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"pgnp\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -10.9813\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.488\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"ulc\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -8.769\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.489\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfco\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -7.9102\n",
      " No. Lags Chosen       = 3\n",
      " Critical value 1%     = -3.49\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.581\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdf\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -10.0351\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.489\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfim\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -9.4059\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.489\n",
      " Critical value 5%     = -2.887\n",
      " Critical value 10%    = -2.58\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfcf\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -6.922\n",
      " No. Lags Chosen       = 5\n",
      " Critical value 1%     = -3.491\n",
      " Critical value 5%     = -2.888\n",
      " Critical value 10%    = -2.581\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gdfce\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -5.1732\n",
      " No. Lags Chosen       = 8\n",
      " Critical value 1%     = -3.492\n",
      " Critical value 5%     = -2.889\n",
      " Critical value 10%    = -2.581\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "37231b5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag Order = 1\n",
      "AIC :  -1.3679402315450668 \n",
      "\n",
      "Lag Order = 2\n",
      "AIC :  -1.621237394447824 \n",
      "\n",
      "Lag Order = 3\n",
      "AIC :  -1.76580083870128 \n",
      "\n",
      "Lag Order = 4\n",
      "AIC :  -2.000735164470319 \n",
      "\n",
      "Lag Order = 5\n",
      "AIC :  -1.9619535608363936 \n",
      "\n",
      "Lag Order = 6\n",
      "AIC :  -2.3303386524829053 \n",
      "\n",
      "Lag Order = 7\n",
      "AIC :  -2.592331352347122 \n",
      "\n",
      "Lag Order = 8\n",
      "AIC :  -3.3172619764582087 \n",
      "\n",
      "Lag Order = 9\n",
      "AIC :  -4.804763125958635 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\skywater\\pycharmprojects\\personal\\py39\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    }
   ],
   "source": [
    "# step7：选择模型阶数并训练，根据AIC值，lag=4时达到局部最优\n",
    "model = VAR(df_differenced)\n",
    "for i in [1,2,3,4,5,6,7,8,9]:\n",
    "    result = model.fit(i)\n",
    "    print('Lag Order =', i)\n",
    "    print('AIC : ', result.aic, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8b0c8e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Thu, 01, Jul, 2021\n",
       "Time:                     18:07:25\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         8.00000    BIC:                    4.37122\n",
       "Nobs:                     113.000    HQIC:                  0.584936\n",
       "Log likelihood:          -905.679    FPE:                   0.155570\n",
       "AIC:                     -2.00074    Det(Omega_mle):       0.0200322\n",
       "--------------------------------------------------------------------\n",
       "Results for equation rgnp\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            2.430021         2.677505            0.908           0.364\n",
       "L1.rgnp         -0.750066         0.159023           -4.717           0.000\n",
       "L1.pgnp         -0.095621         4.938865           -0.019           0.985\n",
       "L1.ulc          -6.213996         4.637452           -1.340           0.180\n",
       "L1.gdfco        -7.414768        10.184884           -0.728           0.467\n",
       "L1.gdf         -24.864063        20.071245           -1.239           0.215\n",
       "L1.gdfim         1.082913         4.309034            0.251           0.802\n",
       "L1.gdfcf        16.327252         5.892522            2.771           0.006\n",
       "L1.gdfce         0.910522         2.476361            0.368           0.713\n",
       "L2.rgnp         -0.568178         0.163971           -3.465           0.001\n",
       "L2.pgnp         -1.156201         4.931931           -0.234           0.815\n",
       "L2.ulc         -11.157111         5.381825           -2.073           0.038\n",
       "L2.gdfco         3.012518        12.928317            0.233           0.816\n",
       "L2.gdf         -18.143523        24.090598           -0.753           0.451\n",
       "L2.gdfim        -4.438115         4.410654           -1.006           0.314\n",
       "L2.gdfcf        13.468228         7.279772            1.850           0.064\n",
       "L2.gdfce         5.130419         2.805310            1.829           0.067\n",
       "L3.rgnp         -0.514985         0.152724           -3.372           0.001\n",
       "L3.pgnp        -11.483607         5.392037           -2.130           0.033\n",
       "L3.ulc         -14.195308         5.188718           -2.736           0.006\n",
       "L3.gdfco       -10.154967        13.105508           -0.775           0.438\n",
       "L3.gdf         -15.438858        21.610822           -0.714           0.475\n",
       "L3.gdfim        -6.405290         4.292790           -1.492           0.136\n",
       "L3.gdfcf         9.217402         7.081652            1.302           0.193\n",
       "L3.gdfce         5.279941         2.833925            1.863           0.062\n",
       "L4.rgnp         -0.166878         0.138786           -1.202           0.229\n",
       "L4.pgnp          5.329900         5.795837            0.920           0.358\n",
       "L4.ulc          -4.834548         5.259608           -0.919           0.358\n",
       "L4.gdfco        10.841602        10.526530            1.030           0.303\n",
       "L4.gdf         -17.651510        18.746673           -0.942           0.346\n",
       "L4.gdfim        -1.971233         4.029415           -0.489           0.625\n",
       "L4.gdfcf         0.617824         5.842684            0.106           0.916\n",
       "L4.gdfce        -2.977187         2.594251           -1.148           0.251\n",
       "===========================================================================\n",
       "\n",
       "Results for equation pgnp\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            0.094556         0.063491            1.489           0.136\n",
       "L1.rgnp         -0.004231         0.003771           -1.122           0.262\n",
       "L1.pgnp          0.082204         0.117114            0.702           0.483\n",
       "L1.ulc          -0.097769         0.109966           -0.889           0.374\n",
       "L1.gdfco        -0.220150         0.241511           -0.912           0.362\n",
       "L1.gdf           0.438078         0.475943            0.920           0.357\n",
       "L1.gdfim        -0.116223         0.102179           -1.137           0.255\n",
       "L1.gdfcf         0.236541         0.139727            1.693           0.090\n",
       "L1.gdfce        -0.026930         0.058721           -0.459           0.647\n",
       "L2.rgnp          0.004056         0.003888            1.043           0.297\n",
       "L2.pgnp         -0.127478         0.116949           -1.090           0.276\n",
       "L2.ulc           0.083656         0.127617            0.656           0.512\n",
       "L2.gdfco        -0.181640         0.306565           -0.593           0.554\n",
       "L2.gdf           0.035376         0.571252            0.062           0.951\n",
       "L2.gdfim        -0.115745         0.104588           -1.107           0.268\n",
       "L2.gdfcf        -0.015993         0.172623           -0.093           0.926\n",
       "L2.gdfce         0.088611         0.066521            1.332           0.183\n",
       "L3.rgnp          0.002509         0.003621            0.693           0.488\n",
       "L3.pgnp         -0.023973         0.127860           -0.187           0.851\n",
       "L3.ulc           0.033721         0.123038            0.274           0.784\n",
       "L3.gdfco        -0.281483         0.310767           -0.906           0.365\n",
       "L3.gdf          -0.214294         0.512450           -0.418           0.676\n",
       "L3.gdfim        -0.127143         0.101794           -1.249           0.212\n",
       "L3.gdfcf         0.028885         0.167925            0.172           0.863\n",
       "L3.gdfce         0.016817         0.067200            0.250           0.802\n",
       "L4.rgnp         -0.000618         0.003291           -0.188           0.851\n",
       "L4.pgnp          0.042486         0.137435            0.309           0.757\n",
       "L4.ulc           0.015547         0.124719            0.125           0.901\n",
       "L4.gdfco        -0.189719         0.249612           -0.760           0.447\n",
       "L4.gdf           0.099973         0.444534            0.225           0.822\n",
       "L4.gdfim        -0.072495         0.095548           -0.759           0.448\n",
       "L4.gdfcf        -0.061357         0.138546           -0.443           0.658\n",
       "L4.gdfce        -0.020523         0.061517           -0.334           0.739\n",
       "===========================================================================\n",
       "\n",
       "Results for equation ulc\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const           -0.053626         0.096020           -0.558           0.577\n",
       "L1.rgnp          0.005745         0.005703            1.007           0.314\n",
       "L1.pgnp         -0.136788         0.177117           -0.772           0.440\n",
       "L1.ulc          -0.495266         0.166308           -2.978           0.003\n",
       "L1.gdfco        -0.397535         0.365249           -1.088           0.276\n",
       "L1.gdf           1.800037         0.719792            2.501           0.012\n",
       "L1.gdfim        -0.130264         0.154530           -0.843           0.399\n",
       "L1.gdfcf        -0.528955         0.211317           -2.503           0.012\n",
       "L1.gdfce        -0.044271         0.088807           -0.499           0.618\n",
       "L2.rgnp          0.011354         0.005880            1.931           0.054\n",
       "L2.pgnp          0.009702         0.176868            0.055           0.956\n",
       "L2.ulc          -0.099997         0.193002           -0.518           0.604\n",
       "L2.gdfco        -0.817458         0.463633           -1.763           0.078\n",
       "L2.gdf           1.117940         0.863933            1.294           0.196\n",
       "L2.gdfim         0.132396         0.158174            0.837           0.403\n",
       "L2.gdfcf        -0.277444         0.261066           -1.063           0.288\n",
       "L2.gdfce        -0.198781         0.100604           -1.976           0.048\n",
       "L3.rgnp          0.011693         0.005477            2.135           0.033\n",
       "L3.pgnp          0.346813         0.193368            1.794           0.073\n",
       "L3.ulc          -0.025547         0.186077           -0.137           0.891\n",
       "L3.gdfco        -0.306628         0.469988           -0.652           0.514\n",
       "L3.gdf           0.986201         0.775004            1.273           0.203\n",
       "L3.gdfim         0.084262         0.153947            0.547           0.584\n",
       "L3.gdfcf        -0.328288         0.253961           -1.293           0.196\n",
       "L3.gdfce        -0.157949         0.101630           -1.554           0.120\n",
       "L4.rgnp          0.004917         0.004977            0.988           0.323\n",
       "L4.pgnp          0.229430         0.207849            1.104           0.270\n",
       "L4.ulc           0.037877         0.188619            0.201           0.841\n",
       "L4.gdfco        -0.470222         0.377501           -1.246           0.213\n",
       "L4.gdf           0.620381         0.672290            0.923           0.356\n",
       "L4.gdfim         0.179095         0.144502            1.239           0.215\n",
       "L4.gdfcf         0.198607         0.209529            0.948           0.343\n",
       "L4.gdfce        -0.036155         0.093035           -0.389           0.698\n",
       "===========================================================================\n",
       "\n",
       "Results for equation gdfco\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            0.040011         0.030021            1.333           0.183\n",
       "L1.rgnp         -0.001920         0.001783           -1.077           0.282\n",
       "L1.pgnp         -0.072381         0.055377           -1.307           0.191\n",
       "L1.ulc          -0.060315         0.051997           -1.160           0.246\n",
       "L1.gdfco        -0.852758         0.114197           -7.467           0.000\n",
       "L1.gdf           0.434064         0.225047            1.929           0.054\n",
       "L1.gdfim         0.037258         0.048315            0.771           0.441\n",
       "L1.gdfcf        -0.092569         0.066069           -1.401           0.161\n",
       "L1.gdfce         0.001254         0.027766            0.045           0.964\n",
       "L2.rgnp         -0.000903         0.001839           -0.491           0.623\n",
       "L2.pgnp         -0.098972         0.055299           -1.790           0.073\n",
       "L2.ulc          -0.020407         0.060343           -0.338           0.735\n",
       "L2.gdfco        -0.802415         0.144958           -5.536           0.000\n",
       "L2.gdf           0.399859         0.270114            1.480           0.139\n",
       "L2.gdfim         0.073417         0.049454            1.485           0.138\n",
       "L2.gdfcf        -0.041899         0.081624           -0.513           0.608\n",
       "L2.gdfce        -0.012623         0.031454           -0.401           0.688\n",
       "L3.rgnp         -0.000841         0.001712           -0.491           0.623\n",
       "L3.pgnp          0.014940         0.060458            0.247           0.805\n",
       "L3.ulc           0.031259         0.058178            0.537           0.591\n",
       "L3.gdfco        -0.475317         0.146945           -3.235           0.001\n",
       "L3.gdf           0.049640         0.242310            0.205           0.838\n",
       "L3.gdfim         0.037832         0.048133            0.786           0.432\n",
       "L3.gdfcf         0.001531         0.079403            0.019           0.985\n",
       "L3.gdfce         0.009834         0.031775            0.309           0.757\n",
       "L4.rgnp          0.000502         0.001556            0.323           0.747\n",
       "L4.pgnp          0.021677         0.064985            0.334           0.739\n",
       "L4.ulc           0.012629         0.058973            0.214           0.830\n",
       "L4.gdfco        -0.281313         0.118028           -2.383           0.017\n",
       "L4.gdf          -0.152831         0.210196           -0.727           0.467\n",
       "L4.gdfim         0.044867         0.045180            0.993           0.321\n",
       "L4.gdfcf         0.062066         0.065511            0.947           0.343\n",
       "L4.gdfce        -0.014219         0.029088           -0.489           0.625\n",
       "===========================================================================\n",
       "\n",
       "Results for equation gdf\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            0.007036         0.019365            0.363           0.716\n",
       "L1.rgnp          0.000167         0.001150            0.145           0.885\n",
       "L1.pgnp          0.032251         0.035720            0.903           0.367\n",
       "L1.ulc          -0.005285         0.033540           -0.158           0.875\n",
       "L1.gdfco         0.013623         0.073661            0.185           0.853\n",
       "L1.gdf          -0.279506         0.145163           -1.925           0.054\n",
       "L1.gdfim         0.027655         0.031165            0.887           0.375\n",
       "L1.gdfcf         0.026646         0.042617            0.625           0.532\n",
       "L1.gdfce        -0.009551         0.017910           -0.533           0.594\n",
       "L2.rgnp          0.000560         0.001186            0.472           0.637\n",
       "L2.pgnp         -0.079633         0.035670           -2.233           0.026\n",
       "L2.ulc          -0.001400         0.038923           -0.036           0.971\n",
       "L2.gdfco        -0.054219         0.093502           -0.580           0.562\n",
       "L2.gdf          -0.010857         0.174232           -0.062           0.950\n",
       "L2.gdfim         0.033689         0.031899            1.056           0.291\n",
       "L2.gdfcf        -0.020837         0.052650           -0.396           0.692\n",
       "L2.gdfce        -0.009148         0.020289           -0.451           0.652\n",
       "L3.rgnp          0.000946         0.001105            0.856           0.392\n",
       "L3.pgnp         -0.037163         0.038997           -0.953           0.341\n",
       "L3.ulc           0.011733         0.037527            0.313           0.755\n",
       "L3.gdfco         0.025826         0.094784            0.272           0.785\n",
       "L3.gdf           0.062331         0.156297            0.399           0.690\n",
       "L3.gdfim         0.076010         0.031047            2.448           0.014\n",
       "L3.gdfcf        -0.040828         0.051217           -0.797           0.425\n",
       "L3.gdfce        -0.033158         0.020496           -1.618           0.106\n",
       "L4.rgnp         -0.000046         0.001004           -0.046           0.963\n",
       "L4.pgnp          0.121837         0.041918            2.907           0.004\n",
       "L4.ulc          -0.071979         0.038039           -1.892           0.058\n",
       "L4.gdfco         0.072031         0.076132            0.946           0.344\n",
       "L4.gdf           0.326105         0.135583            2.405           0.016\n",
       "L4.gdfim         0.058796         0.029142            2.018           0.044\n",
       "L4.gdfcf        -0.038331         0.042256           -0.907           0.364\n",
       "L4.gdfce        -0.061981         0.018763           -3.303           0.001\n",
       "===========================================================================\n",
       "\n",
       "Results for equation gdfim\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const           -0.090111         0.094024           -0.958           0.338\n",
       "L1.rgnp          0.000120         0.005584            0.021           0.983\n",
       "L1.pgnp          0.133455         0.173434            0.769           0.442\n",
       "L1.ulc           0.230635         0.162850            1.416           0.157\n",
       "L1.gdfco         0.153838         0.357655            0.430           0.667\n",
       "L1.gdf           0.989766         0.704827            1.404           0.160\n",
       "L1.gdfim        -0.366913         0.151317           -2.425           0.015\n",
       "L1.gdfcf        -0.023226         0.206923           -0.112           0.911\n",
       "L1.gdfce         0.032370         0.086961            0.372           0.710\n",
       "L2.rgnp         -0.004681         0.005758           -0.813           0.416\n",
       "L2.pgnp          0.286962         0.173191            1.657           0.098\n",
       "L2.ulc           0.062975         0.188990            0.333           0.739\n",
       "L2.gdfco         0.558171         0.453994            1.229           0.219\n",
       "L2.gdf          -0.316201         0.845972           -0.374           0.709\n",
       "L2.gdfim        -0.293256         0.154886           -1.893           0.058\n",
       "L2.gdfcf         0.164223         0.255638            0.642           0.521\n",
       "L2.gdfce         0.038789         0.098512            0.394           0.694\n",
       "L3.rgnp         -0.008393         0.005363           -1.565           0.118\n",
       "L3.pgnp          0.402066         0.189348            2.123           0.034\n",
       "L3.ulc           0.159806         0.182208            0.877           0.380\n",
       "L3.gdfco         0.353693         0.460216            0.769           0.442\n",
       "L3.gdf          -1.272158         0.758891           -1.676           0.094\n",
       "L3.gdfim        -0.231479         0.150747           -1.536           0.125\n",
       "L3.gdfcf         0.460145         0.248681            1.850           0.064\n",
       "L3.gdfce         0.115537         0.099517            1.161           0.246\n",
       "L4.rgnp         -0.005804         0.004874           -1.191           0.234\n",
       "L4.pgnp          0.280189         0.203528            1.377           0.169\n",
       "L4.ulc          -0.042873         0.184698           -0.232           0.816\n",
       "L4.gdfco         0.263797         0.369652            0.714           0.475\n",
       "L4.gdf          -0.665280         0.658313           -1.011           0.312\n",
       "L4.gdfim        -0.032456         0.141498           -0.229           0.819\n",
       "L4.gdfcf         0.312591         0.205173            1.524           0.128\n",
       "L4.gdfce        -0.000055         0.091100           -0.001           1.000\n",
       "===========================================================================\n",
       "\n",
       "Results for equation gdfcf\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const           -0.041178         0.056333           -0.731           0.465\n",
       "L1.rgnp         -0.002572         0.003346           -0.769           0.442\n",
       "L1.pgnp          0.054027         0.103910            0.520           0.603\n",
       "L1.ulc          -0.157556         0.097569           -1.615           0.106\n",
       "L1.gdfco        -0.298011         0.214282           -1.391           0.164\n",
       "L1.gdf           1.328904         0.422284            3.147           0.002\n",
       "L1.gdfim         0.245942         0.090659            2.713           0.007\n",
       "L1.gdfcf        -0.610122         0.123974           -4.921           0.000\n",
       "L1.gdfce        -0.192342         0.052101           -3.692           0.000\n",
       "L2.rgnp         -0.003726         0.003450           -1.080           0.280\n",
       "L2.pgnp          0.088885         0.103764            0.857           0.392\n",
       "L2.ulc          -0.239913         0.113230           -2.119           0.034\n",
       "L2.gdfco        -0.239037         0.272002           -0.879           0.380\n",
       "L2.gdf           1.518625         0.506848            2.996           0.003\n",
       "L2.gdfim         0.033537         0.092797            0.361           0.718\n",
       "L2.gdfcf        -0.663722         0.153161           -4.333           0.000\n",
       "L2.gdfce        -0.140479         0.059022           -2.380           0.017\n",
       "L3.rgnp          0.000131         0.003213            0.041           0.968\n",
       "L3.pgnp          0.055857         0.113444            0.492           0.622\n",
       "L3.ulc          -0.105921         0.109167           -0.970           0.332\n",
       "L3.gdfco        -0.229773         0.275730           -0.833           0.405\n",
       "L3.gdf           0.547686         0.454675            1.205           0.228\n",
       "L3.gdfim         0.270028         0.090317            2.990           0.003\n",
       "L3.gdfcf        -0.152479         0.148993           -1.023           0.306\n",
       "L3.gdfce        -0.163651         0.059624           -2.745           0.006\n",
       "L4.rgnp          0.000518         0.002920            0.177           0.859\n",
       "L4.pgnp          0.359919         0.121940            2.952           0.003\n",
       "L4.ulc          -0.190278         0.110658           -1.720           0.086\n",
       "L4.gdfco        -0.043476         0.221470           -0.196           0.844\n",
       "L4.gdf           0.440413         0.394416            1.117           0.264\n",
       "L4.gdfim         0.074793         0.084776            0.882           0.378\n",
       "L4.gdfcf        -0.280249         0.122926           -2.280           0.023\n",
       "L4.gdfce        -0.181084         0.054581           -3.318           0.001\n",
       "===========================================================================\n",
       "\n",
       "Results for equation gdfce\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const           -0.141012         0.157361           -0.896           0.370\n",
       "L1.rgnp         -0.004339         0.009346           -0.464           0.642\n",
       "L1.pgnp          0.460153         0.290265            1.585           0.113\n",
       "L1.ulc          -0.066122         0.272551           -0.243           0.808\n",
       "L1.gdfco        -0.062414         0.598583           -0.104           0.917\n",
       "L1.gdf           1.371788         1.179621            1.163           0.245\n",
       "L1.gdfim         0.127465         0.253249            0.503           0.615\n",
       "L1.gdfcf         0.071207         0.346313            0.206           0.837\n",
       "L1.gdfce        -0.308256         0.145540           -2.118           0.034\n",
       "L2.rgnp         -0.009196         0.009637           -0.954           0.340\n",
       "L2.pgnp          0.412986         0.289858            1.425           0.154\n",
       "L2.ulc          -0.399569         0.316299           -1.263           0.206\n",
       "L2.gdfco        -0.331334         0.759819           -0.436           0.663\n",
       "L2.gdf           1.528254         1.415845            1.079           0.280\n",
       "L2.gdfim         0.185624         0.259222            0.716           0.474\n",
       "L2.gdfcf         0.187718         0.427845            0.439           0.661\n",
       "L2.gdfce        -0.359564         0.164873           -2.181           0.029\n",
       "L3.rgnp         -0.015314         0.008976           -1.706           0.088\n",
       "L3.pgnp         -0.115675         0.316899           -0.365           0.715\n",
       "L3.ulc           0.124086         0.304950            0.407           0.684\n",
       "L3.gdfco        -0.396933         0.770233           -0.515           0.606\n",
       "L3.gdf           0.932961         1.270105            0.735           0.463\n",
       "L3.gdfim        -0.068789         0.252295           -0.273           0.785\n",
       "L3.gdfcf        -0.126047         0.416201           -0.303           0.762\n",
       "L3.gdfce        -0.105270         0.166555           -0.632           0.527\n",
       "L4.rgnp         -0.009219         0.008157           -1.130           0.258\n",
       "L4.pgnp          0.448445         0.340631            1.317           0.188\n",
       "L4.ulc          -0.428451         0.309116           -1.386           0.166\n",
       "L4.gdfco        -0.155466         0.618662           -0.251           0.802\n",
       "L4.gdf           3.387650         1.101774            3.075           0.002\n",
       "L4.gdfim         0.076950         0.236816            0.325           0.745\n",
       "L4.gdfcf        -0.045155         0.343384           -0.132           0.895\n",
       "L4.gdfce        -0.473155         0.152468           -3.103           0.002\n",
       "===========================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "             rgnp      pgnp       ulc     gdfco       gdf     gdfim     gdfcf     gdfce\n",
       "rgnp     1.000000  0.248342 -0.668492 -0.160133 -0.047777  0.084925  0.009962  0.205557\n",
       "pgnp     0.248342  1.000000 -0.148392 -0.167766 -0.134896  0.007830 -0.169435  0.032134\n",
       "ulc     -0.668492 -0.148392  1.000000  0.268127  0.327761  0.171497  0.135410 -0.026037\n",
       "gdfco   -0.160133 -0.167766  0.268127  1.000000  0.303563  0.232997 -0.035042  0.184834\n",
       "gdf     -0.047777 -0.134896  0.327761  0.303563  1.000000  0.196670  0.446012  0.309277\n",
       "gdfim    0.084925  0.007830  0.171497  0.232997  0.196670  1.000000 -0.089852  0.707809\n",
       "gdfcf    0.009962 -0.169435  0.135410 -0.035042  0.446012 -0.089852  1.000000 -0.197099\n",
       "gdfce    0.205557  0.032134 -0.026037  0.184834  0.309277  0.707809 -0.197099  1.000000\n",
       "\n"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择lag=4拟合模型\n",
    "model_fitted = model.fit(4)\n",
    "model_fitted.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "77b94b4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rgnp   : 2.09\n",
      "pgnp   : 2.02\n",
      "ulc    : 2.17\n",
      "gdfco  : 2.05\n",
      "gdf    : 2.25\n",
      "gdfim  : 1.99\n",
      "gdfcf  : 2.2\n",
      "gdfce  : 2.17\n"
     ]
    }
   ],
   "source": [
    "# step8：durbin watson test，检验残差项中是否还存在相关性，这一步的目的是确保模型已经解释了数据中所有的方差和模式\n",
    "out = durbin_watson(model_fitted.resid)\n",
    "for col, val in zip(df.columns, out):\n",
    "    print(adjust(col), ':', round(val, 2))  # 检验值越接近2，说明模型越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "1eafbddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rgnp_2d</th>\n",
       "      <th>pgnp_2d</th>\n",
       "      <th>ulc_2d</th>\n",
       "      <th>gdfco_2d</th>\n",
       "      <th>gdf_2d</th>\n",
       "      <th>gdfim_2d</th>\n",
       "      <th>gdfcf_2d</th>\n",
       "      <th>gdfce_2d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-10-01</th>\n",
       "      <td>48.322456</td>\n",
       "      <td>1.250774</td>\n",
       "      <td>0.595993</td>\n",
       "      <td>0.265657</td>\n",
       "      <td>-0.104146</td>\n",
       "      <td>0.304119</td>\n",
       "      <td>-0.917227</td>\n",
       "      <td>-0.113061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>-34.962286</td>\n",
       "      <td>-0.387966</td>\n",
       "      <td>-0.329877</td>\n",
       "      <td>-0.042217</td>\n",
       "      <td>0.164633</td>\n",
       "      <td>1.357223</td>\n",
       "      <td>0.618163</td>\n",
       "      <td>3.029975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-04-01</th>\n",
       "      <td>20.392680</td>\n",
       "      <td>0.291298</td>\n",
       "      <td>0.390812</td>\n",
       "      <td>-0.134488</td>\n",
       "      <td>-0.486073</td>\n",
       "      <td>-0.149551</td>\n",
       "      <td>-1.238234</td>\n",
       "      <td>-2.345223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-07-01</th>\n",
       "      <td>-37.416599</td>\n",
       "      <td>-0.280943</td>\n",
       "      <td>0.367912</td>\n",
       "      <td>0.102797</td>\n",
       "      <td>0.333371</td>\n",
       "      <td>-0.502103</td>\n",
       "      <td>0.469468</td>\n",
       "      <td>0.517424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rgnp_2d   pgnp_2d    ulc_2d  gdfco_2d    gdf_2d  gdfim_2d  \\\n",
       "date                                                                      \n",
       "1988-10-01  48.322456  1.250774  0.595993  0.265657 -0.104146  0.304119   \n",
       "1989-01-01 -34.962286 -0.387966 -0.329877 -0.042217  0.164633  1.357223   \n",
       "1989-04-01  20.392680  0.291298  0.390812 -0.134488 -0.486073 -0.149551   \n",
       "1989-07-01 -37.416599 -0.280943  0.367912  0.102797  0.333371 -0.502103   \n",
       "\n",
       "            gdfcf_2d  gdfce_2d  \n",
       "date                            \n",
       "1988-10-01 -0.917227 -0.113061  \n",
       "1989-01-01  0.618163  3.029975  \n",
       "1989-04-01 -1.238234 -2.345223  \n",
       "1989-07-01  0.469468  0.517424  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step9：模型已经足够使用了，下一步进行预测\n",
    "lag_order = model_fitted.k_ar\n",
    "forecast_input = df_differenced.values[-lag_order:]\n",
    "fc = model_fitted.forecast(y=forecast_input, steps=nobs)\n",
    "df_forecast = pd.DataFrame(fc, index=df.index[-nobs:], columns=df.columns + '_2d')\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "4e8cee88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step10：将差分后的值还原为原数据\n",
    "def invert_transformation(df_train, df_forecast):\n",
    "    df_fc = df_forecast.copy()\n",
    "    columns = df_train.columns\n",
    "    for col in columns: \n",
    "        # 写一个长度为6的数列，用纸笔写出差分的计算过程，可以帮助理解下面这两行还原过程\n",
    "        df_fc[str(col)+'_1d'] = (df_train[col].iloc[-1]-df_train[col].iloc[-2]) + df_fc[str(col)+'_2d'].cumsum()\n",
    "        df_fc[str(col)+'_forecast'] = df_train[col].iloc[-1] + df_fc[str(col)+'_1d'].cumsum()\n",
    "    return df_fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "a312e50f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>rgnp_forecast</th>\n",
       "      <th>pgnp_forecast</th>\n",
       "      <th>ulc_forecast</th>\n",
       "      <th>gdfco_forecast</th>\n",
       "      <th>gdf_forecast</th>\n",
       "      <th>gdfim_forecast</th>\n",
       "      <th>gdfcf_forecast</th>\n",
       "      <th>gdfce_forecast</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
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       "      <th>1988-10-01</th>\n",
       "      <td>4123.022456</td>\n",
       "      <td>3996.950774</td>\n",
       "      <td>181.095993</td>\n",
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       "      <td>125.082773</td>\n",
       "      <td>93.186939</td>\n",
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       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>4168.382626</td>\n",
       "      <td>4021.613582</td>\n",
       "      <td>182.262108</td>\n",
       "      <td>134.389097</td>\n",
       "      <td>128.056341</td>\n",
       "      <td>108.365461</td>\n",
       "      <td>127.283708</td>\n",
       "      <td>96.603854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-04-01</th>\n",
       "      <td>4234.135476</td>\n",
       "      <td>4046.567687</td>\n",
       "      <td>183.819036</td>\n",
       "      <td>135.678050</td>\n",
       "      <td>129.230756</td>\n",
       "      <td>109.977252</td>\n",
       "      <td>128.246409</td>\n",
       "      <td>97.675545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-07-01</th>\n",
       "      <td>4262.471728</td>\n",
       "      <td>4071.240850</td>\n",
       "      <td>185.743875</td>\n",
       "      <td>137.069799</td>\n",
       "      <td>130.738542</td>\n",
       "      <td>111.086940</td>\n",
       "      <td>129.678579</td>\n",
       "      <td>99.264661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rgnp_forecast  pgnp_forecast  ulc_forecast  gdfco_forecast  \\\n",
       "date                                                                     \n",
       "1988-10-01    4123.022456    3996.950774    181.095993      132.965657   \n",
       "1989-01-01    4168.382626    4021.613582    182.262108      134.389097   \n",
       "1989-04-01    4234.135476    4046.567687    183.819036      135.678050   \n",
       "1989-07-01    4262.471728    4071.240850    185.743875      137.069799   \n",
       "\n",
       "            gdf_forecast  gdfim_forecast  gdfcf_forecast  gdfce_forecast  \n",
       "date                                                                      \n",
       "1988-10-01    126.395854      106.604119      125.082773       93.186939  \n",
       "1989-01-01    128.056341      108.365461      127.283708       96.603854  \n",
       "1989-04-01    129.230756      109.977252      128.246409       97.675545  \n",
       "1989-07-01    130.738542      111.086940      129.678579       99.264661  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = invert_transformation(df_train, df_forecast)  \n",
    "df_results.loc[:, ['rgnp_forecast', 'pgnp_forecast', 'ulc_forecast', 'gdfco_forecast',\n",
    "                   'gdf_forecast', 'gdfim_forecast', 'gdfcf_forecast', 'gdfce_forecast']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "7d1e2fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=int(len(df.columns)/2), ncols=2, dpi=150, figsize=(10,10))\n",
    "for i, (col,ax) in enumerate(zip(df.columns, axes.flatten())):\n",
    "    df_results[col+'_forecast'].plot(legend=True, ax=ax).autoscale(axis='x',tight=True)\n",
    "    df_test[col][-nobs:].plot(legend=True, ax=ax);\n",
    "    ax.set_title(col + \": Forecast vs Actuals\")\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.spines[\"top\"].set_alpha(0)\n",
    "    ax.tick_params(labelsize=6)\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39746419",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
